Using an eventually consistent dispersed memory to implement storage tiers

ABSTRACT

A method for a dispersed storage network begins by receiving one or more revisions of a data object for storage within a time frame and facilitating, for each revision of the one or more revisions, storage of the revision in the selected primary storage target including at least some encoded data slices of each set of encoded data slices of a plurality of sets of encoded data slices are stored in the selected primary storage target and, for each of the revisions, facilitating subsequent storage of remaining encoded data slices of each set of encoded data slices that were not stored in the selected primary storage target, and determining to store the remaining encoded data slices in another storage target, identifying a most recently stored revision of the data object and facilitating storage of the remaining encoded data slices of the most recently stored revision in the other storage target.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U. S.C. § 120, as a continuation-in-part (CIP) of U.S. Utility patentapplication Ser. No. 14/847,855, entitled “DETERMINISTICALLY SHARING APLURALITY OF PROCESSING RESOURCES,” filed Sep. 8, 2015, which claimspriority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional ApplicationNo. 62/072,123, entitled “ASSIGNING TASK EXECUTION RESOURCES IN ADISPERSED STORAGE NETWORK,” filed Oct. 29, 2014, all of which are herebyincorporated herein by reference in their entirety and made part of thepresent U.S. Utility patent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) in accordance with the present invention; and

FIG. 9A is a flowchart illustrating an example of storing data inaccordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-9A. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSTN memory 22for a user device, a group of devices, or for public access andestablishes per vault dispersed storage (DS) error encoding parametersfor a vault. The managing unit 18 facilitates storage of DS errorencoding parameters for each vault by updating registry information ofthe DSN 10, where the registry information may be stored in the DSNmemory 22, a computing device 12-16, the managing unit 18, and/or theintegrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generateper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSTN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (10)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes a fast storage target 450, a storagetarget 452, the network 24 of FIG. 1, and the distributed storage andtask (DST) processing unit 16 (computing device) of FIG. 1. The faststorage target 450 includes a first group of storage units and thestorage target 452 includes a second group of storage units. Eachstorage unit may be implemented utilizing the DST execution unit 36(storage unit) of FIG. 1. Together, the storage units of the faststorage target 450 and the storage target 452 combine to form aninformation dispersal algorithm (IDA) width number of storage units as aset of storage units for storage of sets of encoded data slices, wherethe IDA width is greater than or equal to twice a decode thresholdassociated with the IDA (e.g., a so-called eventual consistencyconfiguration). Each of the fast storage target 450 and the storagetarget 452 include at least a decode threshold number of storage units.The fast storage target 450 and storage target 452 may be implemented atdifferent sites of the DSN.

The DSN is operable to store data in the storage units as sets ofencoded data slices. In an example of operation of the storing of thedata, the DST processing unit 16 receives one or more revisions of thedata object for storage within a time frame. For example, the DSTprocessing unit 16 receives a first revision of a data object A at time1, receives a second revision of the data object A at time 2, andreceives a third revision of the data object A at time 3. The receivingmay further include receiving a data identifier of the data object and arevision identifier associated with the revision of the data object.

Having received a revision of the data object, the DST processing unit16 selects a primary storage target from a plurality of storage targets.The selecting may be based on one or more of performance levels ofstorage units of the storage targets. For example, the DST processingunit 16 selects the fast storage target 450 when storage units of thefast storage target are associated with improved performance levels(e.g., higher sustained bandwidth of access, lower access latency times,etc.) as compared to storage units of the storage target.

For each of the revisions, the DST processing unit 16 facilitatesstorage of the revision of the data object in the selected primarystorage target. For example, the DST processing unit 16 dispersedstorage error encodes the revision of the data object to produce aplurality of sets of encoded data slices, and sends, for each set ofencoded data slices, at least some of the encoded data slices to storageunits of the selected primary storage target. For instance, the DSTprocessing unit 16 produces the plurality of sets of encoded data slicesto include 18 encoded data slices in each set and sends, via the network24, encoded data slices 1-9 of each of the plurality of sets of encodeddata slices of the revision to the storage units 1-9 of the fast storagetarget for storage.

For each of the revisions, the DST processing unit 16 facilitatessubsequent storage of remaining encoded data slices of each set ofencoded data slices. The facilitating includes temporarily storing theremaining encoded data slices in a memory of the DST processing unit 16.Having facilitated the subsequent storage, the DST processing unit 16determines whether to store encoded data slices in another storagetarget. The DST processing unit 16 indicates to store the encoded dataslices in the other storage target based on one or more of when atimeframe expires without receiving another revision of the data object,in accordance with a schedule, based on a number of temporarily storedrevisions matching a maximum number of revisions for temporary storage,or receiving a request. For example, the DST processing unit 16determines to store encoded data slices of revision 3 in the storagetarget when the maximum number of revisions for temporary storage isthree.

When storing encoded data slices in the other storage target, the DSTprocessing unit 16 identifies a most recently stored revision of thedata object. The identifying includes at least one of performing alookup, initiating a query, or interpreting a query response. Forexample, the DST processing unit 16 accesses the memory of the DSTprocessing unit 16 and determines that revision 3 of the data object Ais the most recently stored revision.

Having identified the most recently stored revision of the data object,the DST processing unit 16 facilitates storage of the remaining encodeddata slices of each set of encoded data slices associated with the mostrecently stored revision and the data object in storage units of theother storage target. For example, the DST processing unit 16 issues,via the network 24, write slice requests to storage units 10-18 of thestorage target, where the write slice requests include the remainingencoded data slices of each of the set of encoded data slices associatedwith revision 3 of the data object.

FIG. 9A is a flowchart illustrating an example of storing data. Inparticular, a method is presented for use in conjunction with one ormore functions and features described in conjunction with FIGS. 1-2,3-8, and also FIG. 9.

The method begins or continues at step 456 where a processing module(e.g., of a distributed storage and task (DST) client unit) receives oneor more revisions of a data object for storage within a time frame. Thereceiving may further include receiving a revision identifier for eachrevision. The method continues at step 458 where the processing moduleselects a primary storage target from a plurality of storage targets.The selecting may be based on identifying a storage target associatedwith a favorable performance level (e.g., best performance, performancegreater than a minimum performance threshold level, etc.) as the primarystorage target.

For each revision, the method continues at step 460 where the processingmodule facilitates storage of the revision in the selected primarystorage target where at least some of the encoded data slices of eachset of encoded data slices of a plurality of sets of encoded data slicesare stored in the selected primary storage target. For example, theprocessing module dispersed storage error encodes the revision of thedata object to produce a plurality of sets of encoded data slices andfor each set, identifies encoded data slices associated with the primarystorage target (e.g., slices corresponding to storage units of theprimary storage target, where a number of storage units of the primarystorage target is greater than or equal to a decode threshold numberassociated with the dispersed storage error coding), and sends theidentified encoded data slices to the storage units of the primarystorage target for storage.

For each of the revisions, the method continues at step 462 where theprocessing module facilitates subsequent storage of remaining encodeddata slices of each set of encoded data slices that were not stored inthe selected primary storage target. For example, the processing moduletemporarily stores (e.g., in a local memory) the remaining encoded dataslices of each set of encoded data slices, stores the revisionindicator, and stores the timestamp.

The method continues at step 464 where the processing module determinesto store the remaining encoded data slices in another storage target.For example, the processing module indicates to store the remainingencoded data slices when a timeframe expires without receiving anotherrevision of the data object. As another example, the processing moduleindicates to store the remaining encoded data slices in accordance witha schedule. As yet another example, the processing module indicates tostore the remaining encoded data slices when a number of temporarilystored revisions is substantially the same as a maximum number of storedrevisions. The determining to store the remaining encoded data slicesand the other storage target further includes identifying the otherstorage target based on at least one of a lookup and performing a query.For example, the processing module identifies the other storage targetas a storage target associated with the selected primary storage target.

The method continues at step 466 where the processing module identifiesa most recently stored revision of the data object. The identifyingincludes at least one of interpreting a lookup, issuing a list slicerequest to a storage unit of the selected primary storage target, orinterpreting a list slice response. The method continues at step 468where the processing module facilitates storage of the remaining encodeddata slices of the most recently stored revision in the other storagetarget. For example, the processing module sends the remaining encodeddata slices of each set of encoded data slices of the plurality of setsof encoded data slices associated with the most recently stored revisionto storage units of the other storage target.

The method described above in conjunction with the processing module canalternatively be performed by other modules of the dispersed storagenetwork or by other computing devices. In addition, at least one memorysection (e.g., a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices of the dispersedstorage network (DSN), cause the one or more computing devices toperform any or all of the method steps described above.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by one or more processingmodules of one or more computing devices of a dispersed storage network(DSN), the method comprises: receiving one or more revisions of a dataobject for storage within a time frame; selecting a primary storagetarget from a plurality of storage targets; for each revision of the oneor more revisions, facilitating storage of the revision in the selectedprimary storage target, where, for the data object, at least someencoded data slices of each set of encoded data slices of a plurality ofsets of encoded data slices are stored in the selected primary storagetarget; and for each of the revisions, facilitating subsequent storageof remaining encoded data slices of each set of encoded data slices thatwere not stored in the selected primary storage target; and determiningto store the remaining encoded data slices in another storage target;identifying a most recently stored revision of the data object; andfacilitating storage of the remaining encoded data slices of the mostrecently stored revision in the other storage target.
 2. The method ofclaim 1, wherein the receiving further includes receiving a revisionidentifier for each revision of the one or more revisions.
 3. The methodof claim 1, wherein the selecting is based on identifying a storagetarget associated with a favorable performance level as the primarystorage target.
 4. The method of claim 3, wherein the favorableperformance level includes any of: best performance or performancegreater than a minimum performance threshold level.
 5. The method ofclaim 1, wherein the facilitating storage of the revision in theselected primary storage target includes: disperse storage errorencoding the revision of the data object to produce a plurality of setsof encoded data slices and for each set, identifying encoded data slicesassociated with the primary storage target, and sending the identifiedencoded data slices to storage units of the primary storage target forstorage.
 6. The method of claim 5, wherein the primary storage targetincludes encoded data slices corresponding to storage units of theprimary storage target, where a number of storage units of the primarystorage target is greater than or equal to a decode threshold numberassociated with the dispersed storage error encoding.
 7. The method ofclaim 1, wherein the facilitating subsequent storage of remainingencoded data slices includes any of: temporarily storing the remainingencoded data slices of each set of encoded data slices, storing theremaining encoded data slices of each set of encoded data slices inlocal memory, storing a revision indicator, or storing a timestamp. 8.The method of claim 1, wherein the determining includes any of:indicating to store the remaining encoded data slices when a timeframeexpires without receiving another revision of the data object;indicating to store the remaining encoded data slices in accordance witha schedule; or indicating to store the remaining encoded data sliceswhen a number of temporarily stored revisions is substantially the sameas a maximum number of stored revisions.
 9. The method of claim 1,wherein the determining to store the remaining encoded data slices andthe other storage target further includes identifying the other storagetarget based on at least one of a lookup, performing a query, orinterpreting a query response.
 10. The method of claim 1, wherein theidentifying the other storage target is based on at least one of alookup and performing a query and includes identifying the other storagetarget as a storage target associated with the selected primary storagetarget.
 11. The method of claim 1, wherein the identifying a mostrecently stored revision of the data object includes at least one ofinterpreting a lookup, issuing a list slice request to a storage unit ofthe selected primary storage target, or interpreting a list sliceresponse.
 12. The method of claim 1, wherein the facilitating storage ofthe remaining encoded data slices includes sending the remaining encodeddata slices of each set of encoded data slices of the plurality of setsof encoded data slices associated with the most recently stored revisionto storage units of the other storage target.
 13. A computing device ofa group of computing devices of a dispersed storage network (DSN), thecomputing device comprises: an interface; a local memory; and aprocessing module operably coupled to the interface and the localmemory, wherein the processing module functions to: receive one or morerevisions of a data object for storage within a time frame; select aprimary storage target from a plurality of storage targets; for eachrevision of the one or more revisions, facilitate storage of therevision in the selected primary storage target, where, for the dataobject, at least some encoded data slices of each set of encoded dataslices of a plurality of sets of encoded data slices are stored in theselected primary storage target; and for each of the revisions,facilitate subsequent storage of remaining encoded data slices of eachset of encoded data slices that were not stored in the selected primarystorage target; and determine to store the remaining encoded data slicesin another storage target; identify a most recently stored revision ofthe data object; and facilitate storage of the remaining encoded dataslices of the most recently stored revision in the other storage target.14. The computing device of claim 13, wherein the selecting is based onidentifying a storage target associated with a favorable performancelevel as the primary storage target.
 15. The computing device of claim13, wherein the facilitate storage of the revision in the selectedprimary storage target includes: disperse storage error encoding therevision of the data object to produce a plurality of sets of encodeddata slices and for each set, identifying encoded data slices associatedwith the primary storage target, and sending the identified encoded dataslices to storage units of the primary storage target for storage. 16.The computing device of claim 15, wherein the primary storage targetincludes encoded data slices corresponding to storage units of theprimary storage target, where a number of storage units of the primarystorage target is greater than or equal to a decode threshold numberassociated with the dispersed storage error encoding.
 17. The computingdevice of claim 13, wherein the facilitating subsequent storage ofremaining encoded data slices includes any of: temporarily storing theremaining encoded data slices of each set of encoded data slices,storing the remaining encoded data slices of each set of encoded dataslices in local memory, storing a revision indicator, or storing atimestamp.
 18. The computing device of claim 13, wherein the determiningincludes any of: indicating to store the remaining encoded data sliceswhen a timeframe expires without receiving another revision of the dataobject; indicating to store the remaining encoded data slices inaccordance with a schedule; or indicating to store the remaining encodeddata slices when a number of temporarily stored revisions issubstantially the same as a maximum number of stored revisions.
 19. Thecomputing device of claim 13, wherein the facilitating storage of theremaining encoded data slices includes sending the remaining encodeddata slices of each set of encoded data slices of the plurality of setsof encoded data slices associated with the most recently stored revisionto storage units of the other storage target.
 20. A system comprises: aninterface; a local memory; and a processing module operably coupled tothe interface and the local memory, wherein the processing modulefunctions to: receive one or more revisions of a data object for storagewithin a time frame; select a primary storage target from a plurality ofstorage targets; for each revision of the one or more revisions,facilitate storage of the revision in the selected primary storagetarget, where, for the data object, at least some encoded data slices ofeach set of encoded data slices of a plurality of sets of encoded dataslices are stored in the selected primary storage target; and for eachof the revisions, facilitate subsequent storage of remaining encodeddata slices of each set of encoded data slices that were not stored inthe selected primary storage target; and determine to store theremaining encoded data slices in another storage target; identify a mostrecently stored revision of the data object; and facilitate storage ofthe remaining encoded data slices of the most recently stored revisionin the other storage target.